Rethinking infectious disease control with occupational targeted
strategies
Data-based simulations point to simpler methods for containing pandemic
spread while minimizing economic disruptions

Date:
February 22, 2022
Source:
Max Planck Institute for Human Development
Summary:
Physical distancing policies and particularly stay-at-home work
mandates have proven highly effective at slowing the spread of
the COVID-19 virus.

But these measures have had numerous unwanted consequences,
including dramatic reductions in economic productivity. Are there
alternative methods that have the potential to simultaneously
contain pandemic spread while also minimizing negative economic
effects? Researchers examined this question using data and methods
commonly excluded from pandemic- control policy design.



FULL STORY ========================================================================== Physical distancing policies and particularly stay-at-home work mandates
have proven highly effective at slowing the spread of the COVID-19
virus. But these measures have had numerous unwanted consequences,
including dramatic reductions in economic productivity. Are there
alternative methods that have the potential to simultaneously
contain pandemic spread while also minimizing negative economic
effects? Researchers at the Max Planck Institute for Human Development
examined this question using data and methods commonly excluded from
pandemic- control policy design. Their findings were published in
Scientific Reports.


========================================================================== Throughout the COVID-19 pandemic, the chief non-pharmaceutical
intervention has been physical distancing, including widespread closure
of shared workspaces and a concomitant shift to remote work where
possible. These measures are not only disruptive to workers, workplaces
and economies, but also likely to cause long- term shifts in working
patterns. Their economic costs have been significant, including losses
in working hours and a drop in global Gross domestic product (GDP),
the full magnitude of which will not be known until the pandemic is over.

Researchers at the Max Planck Institute for Human Development investigated
the efficacy of various pandemic containment measures through data-based simulations. By focusing on occupational interventions, and using
detailed data on the distribution of the workforce across occupations,
wage and workplace proximity, they were able to model the economic
impact of particular containment strategies alongside each intervention's epidemiological impact.

"We conducted simulations of how diseases such as COVID-19
spread primarily through a workforce, rather than just through an indistinguishable population of people, which is a simplification that
people often make," explains Alex Rutherford, senior research scientist
and principal investigator at the Center for Humans and Machines at the
Max Planck Institute for Human Development and co-lead author of the
study. "We saw that the nature of one's job had strongly affected the
outcome of the pandemic." The team used public data on jobs to assign a 'proximity score' to each occupation. This reflected how many people a
given worker was likely to be in contact with. From this they built a
'contact network' showing how an infectious disease such as COVID-19
spreads from person to person.

The data was from New York City, treated as a paradigmatic urban setting,
and include both occupational information and data from public databases,
such as the "Occupational Information Network" (O*NET), which collects occupational data and statistical and economic information from the
United States. Such categories of data rarely figure in the design of
pandemic control policies.

Using data on salaries, the number of people doing each job in NYC and
whether they can work from home, the team measured the social and economic effects an epidemic has specifically due to the actions taken to try to
stop it. The social effects are based on how many people get infected
and the economic costs are based on how many people are furloughed and
have their salary covered because they can't work from home.

The researchers compared how effective various contact reduction
interventions were on lessening the impact of the epidemic; socially
and economically. These ranged from no intervention to very complex
measures based on the structure of the contact network of the respective professional group.

"Our findings demonstrate that the structure of the contact network
heavily influences disease dynamics in non-trivial ways," explains
Demetris Avraam, first author of the study and postdoctoral researcher at
the Center for Humans and Machines at the Max-Planck-Institute for Human Development. For example, furloughing a small proportion of workers can
lead to pruning of the network in such a way that the epidemic persists
for a long time, albeit at low levels, leading to a long and costly
furlough. Intuitive strategies such as furloughing workers based on the essentialness of their job, by wage or at random all perform poorly
on this basis. In contrast, network-based metrics such as degree and
centrality are able to reduce the peak of the infection (flattening the
curve) and also reduce the epidemic duration.

The researchers found that the basic strategy of worker removal according
to the number of close personal contacts that worker has, performs approximately the same as more complex metrics based on complete network structure or other occupational characteristics.

"In practice, the number of contacts could be estimated simply using
a smartphone app that estimates Bluetooth proximity to other terminals
without tracking IDs," says study co-lead author Manuel Cebrian, Leader
of the Digital Mobilization Group at the Center for Humans and Machines at
the Max Planck Institute for Human Development. His research has included
how smartphone data and tracing apps can be used for pandemic response.

The COVID-19 pandemic has caused many profound societal changes that are unlikely to be reversed even once the disease abates. This includes
vast changes in demand across sectors, the large-scale adoption
of remote working and challenging deeply ingrained understandings
of workplaces. This has also implications for future automation of
jobs. Automation processes are increasingly used in occupations with a
high degree of contact with others. For example, online consultations
with doctors or online trainings in sports and education are on the rise.

special promotion Explore the latest scientific research on sleep and
dreams in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided by
Max_Planck_Institute_for_Human_Development. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Demetris Avraam, Nick Obradovich, Niccolo` Pescetelli, Manuel
Cebrian,
Alex Rutherford. The network limits of infectious disease control
via occupation-based targeting. Scientific Reports, 2021; 11 (1)
DOI: 10.1038/s41598-021-02226-x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220222134219.htm

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